{"title"=>"The Contribution of Social Behaviour to the Transmission of Influenza A in a Human Population", "type"=>"journal", "authors"=>[{"first_name"=>"Adam J.", "last_name"=>"Kucharski", "scopus_author_id"=>"54883807400"}, {"first_name"=>"Kin O.", "last_name"=>"Kwok", "scopus_author_id"=>"35983448100"}, {"first_name"=>"Vivian W I", "last_name"=>"Wei", "scopus_author_id"=>"56237605400"}, {"first_name"=>"Benjamin J.", "last_name"=>"Cowling", "scopus_author_id"=>"8644765500"}, {"first_name"=>"Jonathan M.", "last_name"=>"Read", "scopus_author_id"=>"36761595400"}, {"first_name"=>"Justin", "last_name"=>"Lessler", "scopus_author_id"=>"22951309100"}, {"first_name"=>"Derek A.", "last_name"=>"Cummings", "scopus_author_id"=>"9842706300"}, {"first_name"=>"Steven", "last_name"=>"Riley", "scopus_author_id"=>"7102619416"}], "year"=>2014, "source"=>"PLoS Pathogens", "identifiers"=>{"scopus"=>"2-s2.0-84903485799", "sgr"=>"84903485799", "issn"=>"15537374", "doi"=>"10.1371/journal.ppat.1004206", "pmid"=>"24968312", "isbn"=>"1553-7374 (Electronic)\\r1553-7366 (Linking)", "pui"=>"373412688"}, "id"=>"f970c39f-2793-38cf-98fa-baa31cca24e7", "abstract"=>"Variability in the risk of transmission for respiratory pathogens can result from several factors, including the intrinsic properties of the pathogen, the immune state of the host and the host's behaviour. It has been proposed that self-reported social mixing patterns can explain the behavioural component of this variability, with simulated intervention studies based on these data used routinely to inform public health policy. However, in the absence of robust studies with biological endpoints for individuals, it is unclear how age and social behaviour contribute to infection risk. To examine how the structure and nature of social contacts influenced infection risk over the course of a single epidemic, we designed a flexible disease modelling framework: the population was divided into a series of increasingly detailed age and social contact classes, with the transmissibility of each age-contact class determined by the average contacts of that class. Fitting the models to serologically confirmed infection data from the 2009 Hong Kong influenza A/H1N1p pandemic, we found that an individual's risk of infection was influenced strongly by the average reported social mixing behaviour of their age group, rather than by their personal reported contacts. We also identified the resolution of social mixing that shaped transmission: epidemic dynamics were driven by intense contacts between children, a post-childhood drop in risky contacts and a subsequent rise in contacts for individuals aged 35-50. Our results demonstrate that self-reported social contact surveys can account for age-associated heterogeneity in the transmission of a respiratory pathogen in humans, and show robustly how these individual-level behaviours manifest themselves through assortative age groups. Our results suggest it is possible to profile the social structure of different populations and to use these aggregated data to predict their inherent transmission potential.", "link"=>"http://www.mendeley.com/research/contribution-social-behaviour-transmission-influenza-human-population-2", "reader_count"=>27, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Researcher"=>7, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>2, "Student > Master"=>3, "Student > Bachelor"=>1, "Professor"=>1, "Professor > Associate Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Researcher"=>7, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>2, "Student > Master"=>3, "Student > Bachelor"=>1, "Professor"=>1, "Professor > Associate Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Environmental Science"=>2, "Biochemistry, Genetics and Molecular Biology"=>1, "Nursing and Health Professions"=>1, "Mathematics"=>5, "Agricultural and Biological Sciences"=>2, "Medicine and Dentistry"=>4, "Psychology"=>2, "Social Sciences"=>2, "Computer Science"=>1, "Immunology and Microbiology"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Social Sciences"=>{"Social Sciences"=>2}, "Psychology"=>{"Psychology"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>2}, "Computer Science"=>{"Computer Science"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Mathematics"=>{"Mathematics"=>5}, "Unspecified"=>{"Unspecified"=>5}, "Environmental Science"=>{"Environmental Science"=>2}}, "reader_count_by_country"=>{"United States"=>1, "United Kingdom"=>3, "Australia"=>1, "France"=>1}, "group_count"=>2}

{"files"=>["https://ndownloader.figshare.com/files/1568836"], "description"=>"<p>By dividing the population into different numbers of age groups and contact classes, it was possible to recreate a number of commonly used model structures. If only one age groups and one contact classes were included, the framework produced a simple mass-action model, in which all individuals had the same risk of infection. When there was only one contact class in each age group, we obtained an age-structured model. Alternatively, when only one age group was used, risk of infection depended only on the contact class an individual was in.</p>", "links"=>[], "tags"=>["ecology", "microbial ecology", "microbiology", "Virology", "Viral transmission and infection", "Population biology", "Population Dynamics", "epidemiology", "Disease dynamics", "Infectious disease epidemiology"], "article_id"=>1085756, "categories"=>["Biological Sciences"], "users"=>["Adam J. Kucharski", "Kin O. Kwok", "Vivian W. I. Wei", "Benjamin J. Cowling", "Jonathan M. Read", "Justin Lessler", "Derek A. Cummings", "Steven Riley"], "doi"=>"https://dx.doi.org/10.1371/journal.ppat.1004206.g001", "stats"=>{"downloads"=>5, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_of_model_framework_/1085756", "title"=>"Schematic of model framework.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-26 03:23:16"}

{"files"=>["https://ndownloader.figshare.com/files/1568957", "https://ndownloader.figshare.com/files/1568958", "https://ndownloader.figshare.com/files/1568959", "https://ndownloader.figshare.com/files/1568961", "https://ndownloader.figshare.com/files/1568962", "https://ndownloader.figshare.com/files/1568963", "https://ndownloader.figshare.com/files/1568965", "https://ndownloader.figshare.com/files/1568966", "https://ndownloader.figshare.com/files/1568968", "https://ndownloader.figshare.com/files/1568970", "https://ndownloader.figshare.com/files/1568974", "https://ndownloader.figshare.com/files/1568976", "https://ndownloader.figshare.com/files/1568977", "https://ndownloader.figshare.com/files/1568978", "https://ndownloader.figshare.com/files/1568980"], "description"=>"<div><p>Variability in the risk of transmission for respiratory pathogens can result from several factors, including the intrinsic properties of the pathogen, the immune state of the host and the host's behaviour. It has been proposed that self-reported social mixing patterns can explain the behavioural component of this variability, with simulated intervention studies based on these data used routinely to inform public health policy. However, in the absence of robust studies with biological endpoints for individuals, it is unclear how age and social behaviour contribute to infection risk. To examine how the structure and nature of social contacts influenced infection risk over the course of a single epidemic, we designed a flexible disease modelling framework: the population was divided into a series of increasingly detailed age and social contact classes, with the transmissibility of each age-contact class determined by the average contacts of that class. Fitting the models to serologically confirmed infection data from the 2009 Hong Kong influenza A/H1N1p pandemic, we found that an individual's risk of infection was influenced strongly by the average reported social mixing behaviour of their age group, rather than by their personal reported contacts. We also identified the resolution of social mixing that shaped transmission: epidemic dynamics were driven by intense contacts between children, a post-childhood drop in risky contacts and a subsequent rise in contacts for individuals aged 35–50. Our results demonstrate that self-reported social contact surveys can account for age-associated heterogeneity in the transmission of a respiratory pathogen in humans, and show robustly how these individual-level behaviours manifest themselves through assortative age groups. Our results suggest it is possible to profile the social structure of different populations and to use these aggregated data to predict their inherent transmission potential.</p></div>", "links"=>[], "tags"=>["ecology", "microbial ecology", "microbiology", "Virology", "Viral transmission and infection", "Population biology", "Population Dynamics", "epidemiology", "Disease dynamics", "Infectious disease epidemiology", "behaviour", "influenza"], "article_id"=>1085846, "categories"=>["Biological Sciences"], "users"=>["Adam J. Kucharski", "Kin O. Kwok", "Vivian W. I. Wei", "Benjamin J. Cowling", "Jonathan M. Read", "Justin Lessler", "Derek A. Cummings", "Steven Riley"], "doi"=>["https://dx.doi.org/10.1371/journal.ppat.1004206.s001", "https://dx.doi.org/10.1371/journal.ppat.1004206.s002", "https://dx.doi.org/10.1371/journal.ppat.1004206.s003", "https://dx.doi.org/10.1371/journal.ppat.1004206.s004", "https://dx.doi.org/10.1371/journal.ppat.1004206.s005", "https://dx.doi.org/10.1371/journal.ppat.1004206.s006", "https://dx.doi.org/10.1371/journal.ppat.1004206.s007", "https://dx.doi.org/10.1371/journal.ppat.1004206.s008", "https://dx.doi.org/10.1371/journal.ppat.1004206.s009", "https://dx.doi.org/10.1371/journal.ppat.1004206.s010", "https://dx.doi.org/10.1371/journal.ppat.1004206.s011", "https://dx.doi.org/10.1371/journal.ppat.1004206.s012", "https://dx.doi.org/10.1371/journal.ppat.1004206.s013", "https://dx.doi.org/10.1371/journal.ppat.1004206.s014", "https://dx.doi.org/10.1371/journal.ppat.1004206.s015"], "stats"=>{"downloads"=>16, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_Contribution_of_Social_Behaviour_to_the_Transmission_of_Influenza_A_in_a_Human_Population_/1085846", "title"=>"The Contribution of Social Behaviour to the Transmission of Influenza A in a Human Population", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-06-26 03:23:16"}